Intro

In this project, our group set out to take a more in-depth analysis of draft picks over the last 20 years of three of the four major sports leagues in North America. We aimed to see what what places produced the best picks and then what teams in the leagues are the ones making the best valued picks. We pulled our dataset for the NHL, NBA, and the NFL from Kaggle.

NHL

For my dataset I decide to start off by looking at the players who

NFL

Visualizations, particularly interactive ones, will be well-received. That said, do not overuse visualizations. You may be better off with one complicated but well-crafted visualization as opposed to many quick-and-dirty plots. Any plots should be well-thought-out, properly labeled, informative, and visually appealing.

If you want to include dynamic visualizations or tables, you should explore your options from packages that are built from htmlwidgets. These htmlwidgets-based packages offer ways to build lighterweight, dynamic visualizations or tables that don’t require an R server to run! A more complete list of packages is available on the linked website, but a short list includes:

• plotly: Interactive graphics with D3
• leaflet: Interactive maps with OpenStreetMap
• dygraphs: Interactive time series visualization
• visNetwork: Network graph visualization vis.js
• sparkline: Small inline charts
• threejs: Interactive 3D graphics

You may embed a published Shiny app in your blog if useful, but be aware that there is a limited window size for embedded objects, which tends to makes the user experience of the app worse relative to a dedicated Shiny app page. Additionally, Shiny apps will go idle after a few minutes and have to be reloaded by the user, which may also affect the user experience.

Any Shiny apps embedded in your blog should be accompanied by the link to the published Shiny app (I did this using a figure caption in the code chunk below, but you don’t have to incorporate the link in this way).

#{r ds-salaries-app, out.width = “100%”, out.extra = ‘data-external=“1” frameborder=“no”’, fig.cap = “Data Science Salaries Shiny #App”} #knitr::include_app(“https://bebailey.shinyapps.io/data-science-salaries”, # height = “500px”) #```

NBA

library(DT)
mtcars %>% 
  select(mpg, cyl, hp) %>% 
  datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
            filter = 'top',
            options = list(pageLength = 10, autoWidth = TRUE))

library(kableExtra)
summary(cars) %>%
  kbl(col.names = c("Speed", "Distance"),
      row.names = FALSE) %>%
  kable_styling(bootstrap_options = "striped",
                full_width = FALSE) %>%
  row_spec(0, bold = TRUE) %>%
  column_spec(1:2, width = "1.5in") 

Images

Images and gifs can be displayed using code chunks:

“Safe Space” by artist Kenesha Sneed

This is a figure caption

You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.

This is another figure caption

Either way, the file path can be a local path within your project directory or a URL for an image hosted online. This syntax works for PNG, PDF, JPG, and even GIF formats.

Formatting

Customizing your blog design

As a final detail only if you have time, you can explore options for customizing the style of your blog. By default, we are using the readthedown theme from the rmdformats package (see Line 6 of this file if you want to switch out themes).

References

All data sources, any key R packages, and any other sources used in developing your blog should be cited in full in a list of references at the end of your blog. Your blog post should also link to these sources as they are discussed. You may choose any reference style as long as sources are fully cited (try to be consistent!).

Typically, references in R Markdown (and LaTeX) files are incorporated with a BibTeX database (a .bib file). You can try this approach or manually include either a numbered or alphabetized list.

Columbia University has compiled some guidance on how to cite data. Some data sources will give you the citation information to copy and paste. Use the provided citations or citation styles in those cases.

You can list R package citations with the code citation("packageName") in the console and then copy (and reformat as needed) the relevant text, e.g.,

## 
## To cite package 'DT' in publications use:
## 
##   Xie Y, Cheng J, Tan X (2023). _DT: A Wrapper of the JavaScript
##   Library 'DataTables'_. R package version 0.27,
##   <https://CRAN.R-project.org/package=DT>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
##     author = {Yihui Xie and Joe Cheng and Xianying Tan},
##     year = {2023},
##     note = {R package version 0.27},
##     url = {https://CRAN.R-project.org/package=DT},
##   }

The following citations are based on the American Statistical Association citation style (not all of these references are used in this document).:

Baumer, B. S., Kaplan, D. T., and Horton, N. J. (2021), Modern Data Science with R (2nd ed.), Boca Raton, FL: CRC Press.

Broman, K. W. and Woo, K. H. (2018), “Data Organization in Spreadsheets,” The American Statistician, 72:1, 2-10, doi: 10.1080/00031305.2017.1375989

Columbia University Libraries (n.d.), “Data Citation,” available at https://guides.library.columbia.edu/datacitation.

McNamara, A. and Horton N. J. (2018) “Wrangling Categorical Data in R,” The American Statistician, 72:1, 97-104, doi: 10.1080/00031305.2017.1356375.

Shah, Syed A. A. (October 2022), “Starbucks Drinks” (Version 1), Kaggle, available at https://www.kaggle.com/datasets/syedasimalishah/starbucks-drinks.

Xie Y, Cheng J, Tan X (2022). “DT: A Wrapper of the JavaScript Library ‘DataTables’,” R package version 0.24, available at https://CRAN.R-project.org/package=DT.